This is a cross-encoder model fine-tuned on the LangCache sentence pair dataset using the sentence-transformers library, based on the Alibaba-NLP/gte-reranker-modernbert-base model. It is specifically designed to calculate the semantic similarity score between text pairs, aiming to provide efficient text matching and reordering capabilities for the LangCache semantic cache system.

Natural Language ProcessingSentence-transformersEnglish

This is a cross-encoder model fine-tuned by Redis, specifically designed for sentence pair re-ranking in semantic caching scenarios. It is based on the gte-reranker-modernbert-base model of Alibaba-NLP and trained on the LangCache sentence pair dataset. It can efficiently calculate the semantic similarity score between two texts to determine whether they express the same or similar meanings.

Natural Language ProcessingSentence-transformersEnglish

Qwen3-VL-4B-Instruct is a 4-billion-parameter visual language model launched by Alibaba. It is developed based on the Qwen3 architecture and supports multimodal understanding and dialogue tasks. This model has powerful image understanding and text generation capabilities, and can handle complex visual language interaction scenarios.

MultimodalTransformers

Qwen2.5 is a new-generation large language model launched by Alibaba. It has made important optimizations in the chat template, permanently disabled the thinking state, solved problems related to the multi-round chat template, and provided a more convenient experience for experiments and research.

Natural Language ProcessingTransformers

Qwen3-VL is a new-generation vision-language model launched by Alibaba, which has been comprehensively upgraded in text understanding, visual perception, spatial understanding, long context processing, and agent interaction, and supports flexible deployment from edge devices to the cloud.

MultimodalTransformers

Qwen3-VL is the latest generation of vision-language model launched by Alibaba, with significant improvements in text understanding, visual perception, spatial understanding, video analysis, and agent interaction. This model supports multimodal input and has powerful reasoning ability and long context processing ability.

MultimodalTransformers

This is a quantized version of the Alibaba Tongyi Deep Research 30B-A3B model. It uses MXFP4_MOE quantization technology and additionally adds imatrix quantization, aiming to optimize model performance and resource utilization efficiency, and is suitable for text generation tasks.

Natural Language ProcessingGguf

Qwen3-VL-8B-Thinking is an 8-billion-parameter multimodal large language model developed by the Qwen team at Alibaba Cloud. It is designed for deep multimodal reasoning, supporting visual understanding, long context processing, and structured chain-of-thought generation, and performs excellently in complex reasoning tasks.

MultimodalMlx

Qwen3-VL-4B-Instruct is a 4-billion-parameter instruction-tuned multimodal large language model launched by the Alibaba Cloud Qwen team. It is optimized for Qualcomm NPUs, integrating powerful visual-language understanding capabilities with dialogue fine-tuning functions, and is suitable for practical application scenarios such as chat reasoning, document analysis, and visual dialogue.

MultimodalGguf

Qwen3-VL is the latest generation of vision-language model launched by Alibaba. It has excellent text understanding and generation capabilities, in-depth visual perception and reasoning abilities, supports long context processing and video understanding, and provides an instruction-optimized version.

MultimodalSafetensors

This is the GGUF quantized version of Alibaba's Qwen series large language model with 8 billion parameters, designed for advanced reasoning, intelligent agent behavior, and multilingual tasks, and can be used with llama.cpp and compatible tools.

Natural Language ProcessingGgufMultiple Languages

This project is the A3B quantized version of Alibaba's Tongyi In-depth Research 30B model. It aims to reduce the model deployment cost through quantization technology and make knowledge accessible to everyone. This model is optimized based on a large language model with a scale of 30B parameters, maintaining the strong capabilities of the original model while improving the inference efficiency.

Natural Language ProcessingGguf

Qwen2.5-VL-7B-Instruct is a multimodal vision-language model developed by the Tongyi Qianwen team at Alibaba. Based on a parameter scale of 7 billion, it is specifically optimized and trained for visual question-answering tasks. This model can understand and analyze image content and generate accurate natural language answers.

MultimodalSafetensorsEnglish

This is the GGUF quantized version of the Qwen/Qwen3-4B language model, a large language model with 4 billion parameters developed by Alibaba's Tongyi series. It is designed for consumer-grade hardware and supports powerful reasoning, intelligent agent workflows, and multilingual interaction.

Natural Language ProcessingGgufMultiple Languages

Qwen3-Next-80B-A3B-Instruct is a large-scale language model developed by the Tongyi Qianwen team of Alibaba Cloud. It has been optimized with 4-bit quantization using the MLX framework and specifically optimized for Apple chip devices to provide efficient inference capabilities.

Natural Language ProcessingTransformers

Qwen3-Next-80B-A3B-Instruct is the latest generation of large language model developed by the Tongyi Qianwen team at Alibaba. It adopts an innovative hybrid attention mechanism and a highly sparse mixture-of-experts architecture. While maintaining a total of 80B parameters, only 3B parameters are activated, achieving efficient context modeling and inference acceleration. It natively supports a context length of 262K and can be extended to 1M tokens.

Natural Language ProcessingTransformers

Wan-Fun is a powerful text-to-video tool that supports multi-resolution video prediction and multiple languages, meeting diverse video generation needs. This model is based on the Wan2.2 architecture and has a parameter scale of 14B, specifically designed for text-to-video and image-to-video generation tasks.

MultimodalDiffusersMultiple Languages

NVIDIA Qwen2.5-VL-7B-Instruct-FP4 is a quantized version of Alibaba's Qwen2.5-VL-7B-Instruct model. It adopts an optimized Transformer architecture, supports multimodal inputs (text and images), and is suitable for various AI application scenarios. This model undergoes FP4 quantization through the TensorRT Model Optimizer, providing efficient inference performance on NVIDIA GPUs.

MultimodalSafetensors

The NVIDIA Qwen3-32B FP4 model is a quantized version of Alibaba's Qwen3-32B model. It uses an optimized Transformer architecture and performs FP4 quantization on weights and activations. It is suitable for AI applications such as AI agent systems, chatbots, and RAG systems.

Natural Language ProcessingSafetensors

The NVIDIA Qwen3-32B FP4 model is a quantized version of the Alibaba Cloud Qwen3-32B model. It uses an optimized Transformer architecture and achieves efficient inference by quantizing weights and activations to the FP4 data type. This model supports both commercial and non-commercial use and is suitable for various AI application scenarios.

Natural Language ProcessingSafetensors